Observers have little difficulty when asked to localize the center-of-mass of an array of dots. However, it has been shown that subjects show small but systematic biases toward outlying dots in the array (McGowen et al. 1998). Here we replicated this bias toward outlying dots by having subjects estimate the center of mass of dots drawn from 2-D Gaussian distributions. We then predict our results using a standard model of V1 processing that explains how dots in less dense regions of an array have a relatively greater influence on the perceived center of mass. Subjects were presented with arrays of 10 dots drawn from a bivariate Gaussian distribution and were asked to estimate the center of mass with a mouse click. Eye movements were tracked during each trial, allowing us to measure both the end location of the first saccade and the reported estimate of the center of the array. We first estimated the relative influence of dots that fall within differing regions of local dot density by binning dots based on their density and applying linear regression to obtain weights for each density bin. An ideal observer should weigh each dot equally to determine the center of mass. However, we found that dots within regions of lower dot density had the greatest influence on the perceived center. Saccades were also more strongly influenced by dots located in less dense regions. We then developed a simple model that predicts perceived center of mass by convolving the stimulus image with a linear spatial filter, followed by a compressive nonlinearity. The center of mass of the resulting image is naturally pulled toward regions with lower dot density. Fits of this biologically plausible model to our data show that an exponent compressive nonlinearity explains our results well.